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Migdalas, AthanasiosORCID iD iconorcid.org/0000-0001-8473-3663
Publications (10 of 122) Show all publications
Taxidou, A., Karafyllidis, I., Marinaki, M. & Migdalas, A. (2019). A Hybrid Firefly - VNS Algorithm for the Permutation Flowshop Scheduling Problem. In: Angelo Sifaleras, Prof. Said Salhi, Jack Brimberg (Ed.), Variable Neighborhood Search: 6th International Conference, ICVNS 2018, Sithonia, Greece, October 4–7, 2018, Revised Selected Papers. Paper presented at Variable Neighborhood Search, 6th International Conference, ICVNS 2018, Sithonia, Greece, October 4–7, 2018 (pp. 274-286). Springer, 11328
Open this publication in new window or tab >>A Hybrid Firefly - VNS Algorithm for the Permutation Flowshop Scheduling Problem
2019 (English)In: Variable Neighborhood Search: 6th International Conference, ICVNS 2018, Sithonia, Greece, October 4–7, 2018, Revised Selected Papers / [ed] Angelo Sifaleras, Prof. Said Salhi, Jack Brimberg, Springer, 2019, Vol. 11328, p. 274-286Conference paper, Published paper (Refereed)
Abstract [en]

In this paper a Permutation Flowshop Scheduling Problem is solved using a hybridization of the Firefly algorithm with Variable Neighborhood Search algorithm. The Permutation Flowshop Scheduling Problem (PFSP) is one of the most computationally complex problems. It belongs to the class of combinatorial optimization problems characterized as NP-hard. In order to find high quality solutions in reasonable computational time, heuristic and metaheuristic algorithms have been used for solving the problem. The proposed method, Hybrid Firefly Variable Neighborhood Search algorithm, uses in the local search phase of the algorithm a number of local search algorithms, 1-0 relocate, 1-1 exchange and 2-opt. In order to test the effectiveness and efficiency of the proposed method we used a set of benchmark instances of different sizes from the literature.

Place, publisher, year, edition, pages
Springer, 2019
Series
Lecture Notes in Computer Science
Keywords
permutation flowshop scheduling, firefly algorithm, variable neighborhood search
National Category
Other Computer and Information Science Reliability and Maintenance
Research subject
Quality Technology & Management
Identifiers
urn:nbn:se:ltu:diva-73552 (URN)10.1007/978-3-030-15843-0 (DOI)9783030158422 (ISBN)9783030158439 (ISBN)
Conference
Variable Neighborhood Search, 6th International Conference, ICVNS 2018, Sithonia, Greece, October 4–7, 2018
Available from: 2019-04-10 Created: 2019-04-10 Last updated: 2019-04-15Bibliographically approved
Marinakis, Y., Marinaki, M. & Migdalas, A. (2019). A Multi-Adaptive Particle Swarm Optimization for the Vehicle Routing Problem with Time Windows. Information Sciences, 481, 311-329
Open this publication in new window or tab >>A Multi-Adaptive Particle Swarm Optimization for the Vehicle Routing Problem with Time Windows
2019 (English)In: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291, Vol. 481, p. 311-329Article in journal (Refereed) Published
Abstract [en]

In this paper, a new variant of the Particle Swarm Optimization (PSO) algorithm is proposed for the solution of the Vehicle Routing Problem with Time Windows (VRPTW). Three different adaptive strategies are used in the proposed Multi-Adaptive Particle Swarm Optimization (MAPSO) algorithm. The first adaptive strategy concerns the use of a Greedy Randomized Adaptive Search Procedure (GRASP) that is applied when the initial solutions are produced and when a new solution is created during the iterations of the algorithm. The second adaptive strategy concerns the adaptiveness in the movement of the particles from one solution to another where a new adaptive strategy, the Adaptive Combinatorial Neighborhood Topology, is used. Finally, there is an adaptiveness in all parameters of the Particle Swarm Optimization algorithm. The algorithm starts with random values of the parameters and based on some conditions all parameters are adapted during the iterations. The algorithm was tested in the two classic sets of benchmark instances, the one that includes 56 instances with 100 nodes and the other that includes 300 instances with number of nodes varying between 200 and 1000. The algorithm was compared with other versions of PSO and with the best performing algorithms from the literature.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Particle swarm optimization, Vehicle routing problem with time windows, Combinatorial neighborhood topology, Greedy randomized adaptive search procedure, Adaptive strategy
National Category
Reliability and Maintenance
Research subject
Quality Technology & Management
Identifiers
urn:nbn:se:ltu:diva-72530 (URN)10.1016/j.ins.2018.12.086 (DOI)000459846300020 ()2-s2.0-85059469815 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-01-14 (svasva)

Available from: 2019-01-14 Created: 2019-01-14 Last updated: 2019-04-12Bibliographically approved
Migdalas, A. & Pardalos, P. (2018). A Note on Open Problems and Challenges in Optimization Theory and Algorithms. In: Panos M. Pardalos (Ed.), Open Problems in Optimization and Data Analysis: (pp. 1-8). Springer Publishing Company
Open this publication in new window or tab >>A Note on Open Problems and Challenges in Optimization Theory and Algorithms
2018 (English)In: Open Problems in Optimization and Data Analysis / [ed] Panos M. Pardalos, Springer Publishing Company, 2018, p. 1-8Chapter in book (Refereed)
Abstract [en]

Computational and theoretical open problems in optimization, computational geometry, data science, logistics, statistics, supply chain modeling, and data analysis are examined in this book.  Each contribution provides the fundamentals  needed to fully comprehend the impact of individual problems. Current theoretical, algorithmic, and practical methods used to circumvent each problem are provided to stimulate a new effort towards innovative and efficient solutions. Aimed towards graduate students and researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, this book provides a broad comprehensive approach to understanding the significance of specific challenging or open problems within each discipline.

Place, publisher, year, edition, pages
Springer Publishing Company, 2018
Series
Springer Optimization and Its Applications, ISSN 1931-6836 ; 141
Keywords
combinatorial optimization, computational complexity, global optimization, met-heuristics
National Category
Other Natural Sciences Reliability and Maintenance
Identifiers
urn:nbn:se:ltu:diva-73553 (URN)978-3-319-99142-9 (ISBN)978-3-319-99141-2 (ISBN)
Available from: 2019-04-10 Created: 2019-04-10 Last updated: 2019-07-22
Migdalas, A., Zioutas, G. & Chatzinakos, C. (2018). New Statistical Robust Estimators: Open Problems. In: Pardalos, Panos M. (Ed.), Open Problems in Optimization and Data Analysis: (pp. 23-47). Springer Publishing Company
Open this publication in new window or tab >>New Statistical Robust Estimators: Open Problems
2018 (English)In: Open Problems in Optimization and Data Analysis / [ed] Pardalos, Panos M., Springer Publishing Company, 2018, p. 23-47Chapter in book (Refereed)
Abstract [en]

Computational and theoretical open problems in optimization, computational geometry, data science, logistics, statistics, supply chain modeling, and data analysis are examined in this book.  Each contribution provides the fundamentals  needed to fully comprehend the impact of individual problems. Current theoretical, algorithmic, and practical methods used to circumvent each problem are provided to stimulate a new effort towards innovative and efficient solutions. Aimed towards graduate students and researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, this book provides a broad comprehensive approach to understanding the significance of specific challenging or open problems within each discipline.

Place, publisher, year, edition, pages
Springer Publishing Company, 2018
Series
Springer Optimization and Its Application, ISSN 1931-6836 ; 141
Keywords
detecting outliers, robust estimators, regression, covariance, mathematical programming
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:ltu:diva-73555 (URN)978-3-319-99141-2 (ISBN)
Available from: 2019-04-10 Created: 2019-04-10 Last updated: 2019-07-24
Migdalas, A. & Pardalos, P. (2018). Open Problems in Optimization and Data Analysis. Springer Publishing Company
Open this publication in new window or tab >>Open Problems in Optimization and Data Analysis
2018 (English)Book (Refereed)
Abstract [en]

Computational and theoretical open problems in optimization, computational geometry, data science, logistics, statistics, supply chain modeling, and data analysis are examined in this book.  Each contribution provides the fundamentals  needed to fully comprehend the impact of individual problems. Current theoretical, algorithmic, and practical methods used to circumvent each problem are provided to stimulate a new effort towards innovative and efficient solutions. Aimed towards graduate students and researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, this book provides a broad comprehensive approach to understanding the significance of specific challenging or open problems within each discipline.

Place, publisher, year, edition, pages
Springer Publishing Company, 2018. p. 330
Series
Springe Optimization and Its Application, ISSN 1931-6836 ; 141
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:ltu:diva-73550 (URN)10.1007/978-3-319-99142-9 (DOI)978-3-319-99141-2 (ISBN)
Available from: 2019-04-10 Created: 2019-04-10 Last updated: 2019-07-25
Migdalas, A., Karakitsiou, A. & Pardalos, P. (2018). Optimal Location Problems for Electric Vehicles Charging Stations: Models and Challenges. In: Open Problems in Optimization and Data Analysis: (pp. 49-60). Springer Publishing Company
Open this publication in new window or tab >>Optimal Location Problems for Electric Vehicles Charging Stations: Models and Challenges
2018 (English)In: Open Problems in Optimization and Data Analysis, Springer Publishing Company, 2018, p. 49-60Chapter in book (Refereed)
Abstract [en]

Computational and theoretical open problems in optimization, computational geometry, data science, logistics, statistics, supply chain modeling, and data analysis are examined in this book.  Each contribution provides the fundamentals  needed to fully comprehend the impact of individual problems. Current theoretical, algorithmic, and practical methods used to circumvent each problem are provided to stimulate a new effort towards innovative and efficient solutions. Aimed towards graduate students and researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, this book provides a broad comprehensive approach to understanding the significance of specific challenging or open problems within each discipline.

Place, publisher, year, edition, pages
Springer Publishing Company, 2018
Series
Springer Optimization and Its Applications, ISSN 1931-6828 ; 141
Keywords
electric vehicles, charging stations, optimal location, demand forecasting, point demand, flow covering
National Category
Other Environmental Engineering
Identifiers
urn:nbn:se:ltu:diva-73556 (URN)978-3-319-99142-9 (ISBN)
Available from: 2019-04-10 Created: 2019-04-10 Last updated: 2019-07-24
Marinakis, Y., Marinaki, M. & Migdalas, A. (2018). Particle Swarm Optimization for the Vehicle Routing Problem: A Survey and a Comparative Analysis. In: Rafael Martí, Pardalos Panos, Dr. Mauricio G. C. Resende (Ed.), Handbook of Heuristics: . Paper presented at Conference LOT 2014 : Logistics, optimization and transportation 01/09/2014 - 02/09/2014 (pp. 1163-1196). Paper presented at Conference LOT 2014 : Logistics, optimization and transportation 01/09/2014 - 02/09/2014. Cham: Springer
Open this publication in new window or tab >>Particle Swarm Optimization for the Vehicle Routing Problem: A Survey and a Comparative Analysis
2018 (English)In: Handbook of Heuristics / [ed] Rafael Martí, Pardalos Panos, Dr. Mauricio G. C. Resende, Cham: Springer, 2018, p. 1163-1196Chapter in book (Refereed)
Abstract [en]

In the last few years, a number of books and survey papers devoted to the vehicle routing problem (VRP) or to its variants or to the methods used for the solution of one or more variants of the VRP have been published. Also, in these years, the field of swarm intelligence algorithms has a significant growth. One of the most important swarm intelligence algorithms is the particle swarm optimization (PSO). Although the particle swarm optimization was first published in 1995, it took around 10 years in order researchers to publish papers using a PSO algorithm for the solution of variants of the VRP. However, in the last 10 years, a lot of journal papers, conference papers, and book chapters have been published where a variant of VRP is solved using a PSO algorithm. Thus, it is significant to present a survey paper where a review and brief analysis of the most important of these papers will be given. This is the main focus of this chapter.

Place, publisher, year, edition, pages
Cham: Springer, 2018
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Industrial Logistics
Identifiers
urn:nbn:se:ltu:diva-66723 (URN)10.1007/978-3-319-07124-4_42 (DOI)978-3-319-07153-4 (ISBN)978-3-319-07153-4 (ISBN)
Conference
Conference LOT 2014 : Logistics, optimization and transportation 01/09/2014 - 02/09/2014
Available from: 2017-11-23 Created: 2017-11-23 Last updated: 2018-08-22Bibliographically approved
Karakitsiou, A., Kourgiantakis, M., Mavrommati, A. & Migdalas, A. (2018). Regional efficiency evaluation by input-oriented data envelopment analysis of hotel and restaurant sector. Operational Research
Open this publication in new window or tab >>Regional efficiency evaluation by input-oriented data envelopment analysis of hotel and restaurant sector
2018 (English)In: Operational Research, ISSN 1109-2858, E-ISSN 1866-1505Article in journal (Refereed) Epub ahead of print
Abstract [en]

This paper analyses the efficiency of hotel and restaurant sector across all of the thirteen regions in Greece. For our purpose, DEA models were applied in order to evaluate the tourist efficiency and competitiveness of different regions in Greece. The application of this frontier method permits the calculation of efficiency scores based on a series of inputs (number of local units, number of employees and investments) and output (turnover). For the years, 2002–2013, with respectively constant and variable returns to scale models, the empirical analysis shows the differences in the efficiency performance of the Greek regions. More specifically, Attica and South Aegean can be regarded as “moving ahead” regions, whereas some other like, Thessaly, Central Macedonia, Central Greece and Epirus can be considered as “falling further” regions.

Place, publisher, year, edition, pages
Springer, 2018
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Industrial Logistics
Identifiers
urn:nbn:se:ltu:diva-68928 (URN)10.1007/s12351-018-0406-1 (DOI)
Available from: 2018-05-28 Created: 2018-05-28 Last updated: 2018-05-28
Migdalas, A., Marinakis, Y. & Marinakis, M. (2018). Variants and Formulations of the Vehicle Routing Problem. In: Pardalos, Panos M. (Ed.), Open Problems in Optimization and Data Analysis: (pp. 91-127). Springer Publishing Company
Open this publication in new window or tab >>Variants and Formulations of the Vehicle Routing Problem
2018 (English)In: Open Problems in Optimization and Data Analysis / [ed] Pardalos, Panos M., Springer Publishing Company, 2018, p. 91-127Chapter in book (Refereed)
Place, publisher, year, edition, pages
Springer Publishing Company, 2018
Series
Springer Optimization and Its Applications, ISSN 1931-6828 ; 141
Keywords
vehicle routing problem
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:ltu:diva-73558 (URN)978-3-319-99142-9 (ISBN)
Available from: 2019-04-10 Created: 2019-04-10 Last updated: 2019-07-24
Marinakis, Y., Migdalas, A. & Sifaleras, A. (2017). A hybrid Particle Swarm Optimization: Variable Neighborhood Search Algorithm for Constrained Shortest Path Problems. European Journal of Operational Research, 261(3), 819-834
Open this publication in new window or tab >>A hybrid Particle Swarm Optimization: Variable Neighborhood Search Algorithm for Constrained Shortest Path Problems
2017 (English)In: European Journal of Operational Research, ISSN 0377-2217, E-ISSN 1872-6860, Vol. 261, no 3, p. 819-834Article in journal (Refereed) Published
Abstract [en]

In this paper, a well known NP-hard problem, the constrained shortest path problem, is studied. As efficient metaheuristic approaches are required for its solution, a new hybridized version of Particle Swarm Optimization algorithm with Variable Neighborhood Search is proposed for solving this significant combinatorial optimization problem. Particle Swarm Optimization (PSO) is a population-based swarm intelligence algorithm that simulates the social behavior of social organisms by using the physical movements of the particles in the swarm. A Variable Neighborhood Search (VNS) algorithm is applied in order to optimize the particles’ position. In the proposed algorithm, the Particle Swarm Optimization with combined Local and Global Expanding Neighborhood Topology (PSOLGENT), a different equation for the velocities of particles is given and a novel expanding neighborhood topology is used. Another issue in the application of the VNS algorithm in the Constrained Shortest Path problem is which local search algorithms are suitable from this problem. In this paper, a number of continuous local search algorithms are used. The algorithm is tested in a number of modified instances from the TSPLIB and comparisons with classic versions of PSO and with other versions of the proposed method are performed. Also, the results of the algorithm are compared with the results of a number of metaheuristic and evolutionary algorithms. The results obtained are very satisfactory and strengthen the efficiency of the algorithm.

Place, publisher, year, edition, pages
Elsevier, 2017
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Industrial Logistics
Identifiers
urn:nbn:se:ltu:diva-62564 (URN)10.1016/j.ejor.2017.03.031 (DOI)000401889300002 ()2-s2.0-85016465911 (Scopus ID)
Note

Validerad;2017;Nivå 2;2017-06-02 (andbra)

Available from: 2017-03-20 Created: 2017-03-20 Last updated: 2018-09-12Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0001-8473-3663

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